Atmospheric weather forecasting uses a combination of science and technology to predict the state of the atmosphere for a given location. A mix of radar, satellite, forecast models and observational data are all used to assist forecasters. Conventionally, weather observation networks focus on the atmospheric conditions and ignore the area below the horizon where people live and drive. Thus, most weather data is primarily tailored to aviation, and not ground observation. An example of this is a tornado forecasted for a specific area. Real-time data, however, confirming this event is not available from current national weather assets. Forecasters may predict specific atmospheric conditions consistent with a specific weather event, but unfortunately cannot verify that the event is actually taking place, or will take place. Confirmation is left to local storm-chasers or amateur footage and reports that confirm the event by visual means and then report the event to forecasting agencies or local broadcast outlets.
The public, in general, relies heavily on accurate local weather information. The public in the United States (for example) primarily obtain information from local forecasters that decipher atmospheric data provided by government agencies. Emergency management agencies require accurate weather information to respond to local emergencies and provide warnings to the public. Private industries also rely on localized weather data. Private companies pay for weather forecasts tailored to their needs so that they can increase their profits or avoid losses. Weather forecast information has significant relevance for many markets, such as agriculture, transportation and insurance.
State and local governments operate ground-based observation systems which include close circuit television (CCTV) capabilities for winter road maintenance and traffic control. Local departments of transportation usually collect road and weather data from two or more sources, such as the National Weather Service (NMS) and Road Weather Information Systems (RWIS), and generate information for winter maintenance. Traffic CCTV cameras, however, are used primarily for traffic control. These CCTV cameras may be numerous, but are not coordinated for weather events nor do they have the intelligence to determine weather events without human “eyes-on-target”.
The inability to observe weather events in real-time from a ground perspective produces uncertainty and results in ineffective response from emergency responders and decreased confidence from the general public. National data providers (NOAA, NWS) primarily produce atmospheric modeling and data which focuses on global views of weather, making local verification of events at a meso-scale nearly impossible. For example, two areas under the same weather warning may experience two different actual events. The lack of small-scale, real-time verification prevents effective warnings to the local community.
Finally, the above approaches fail to exploit an inherent benefit of a coordinated ground-based visual observation system. Visually being able to observe an event in real-time is a critical aspect for providing validation to atmospheric models and forecasts. Visual systems also allow sensing across a wide area.
As will be explained, the present invention solves the aforementioned deficiencies by autonomously maintaining persistent surveillance of weather events over a selected region of interest on a continuing basis, as well as selecting, controlling and tasking imaging assets to view the weather events. The present invention also provides real-time confirmation of weather events at a hyper-local level.